Remote sensing image target detection and identification based on deep learning
SHI Wenxu1,2, BAO Jiahui3, YAO Yu1,2
1. Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu Sichuan 610081, China; 2. University of Chinese Academy of Sciences, Beijing 100049, China; 3. Glasgow College, University of Electronic Science and Technology of China, Chengdu Sichuan 611731, China
Abstract：In order to improve the precision and speed of existing remote sensing image target detection algorithms in small-scale target detection, a remote sensing image target detection and identification algorithm based on deep learning was proposed. Firstly, a dataset of remote sensing images with different scales was constructed for model training and testing. Secondly, based on the original Single Shot multibox Detector (SSD) network model, the shallow feature fusion module, shallow feature enhancement module and deep feature enhancement module were designed and fused. Finally, the focal loss function was introduced into the training strategy to solve the problem of the imbalance of positive and negative samples in the training process, and the experiment was carried out on the remote sensing image dataset. Experimental results on high-resolution remote sensing image dataset show that the detection mean Average Precision (mAP) of the proposed algorithm achieves 77.95%, which is 3.99 percentage points higher than that of SSD network model, and has the detection speed of 33.8 frame/s. In the extended experiment, the performance of the proposed algorithm is better than that of SSD network model for the detection of fuzzy targets in high-resolution remote sensing images. Experimental results show that the proposed algorithm can effectively improve the precision of remote sensing image target detection.
史文旭, 鲍佳慧, 姚宇. 基于深度学习的遥感图像目标检测与识别[J]. 计算机应用, 2020, 40(12): 3558-3562.
SHI Wenxu, BAO Jiahui, YAO Yu. Remote sensing image target detection and identification based on deep learning. Journal of Computer Applications, 2020, 40(12): 3558-3562.
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2020, Vol. 40 
